



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Optional[int]     )	-> Dict:






	"""simple docstring"""





	for attribute in key.split("."     ):
		A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     )

	if weight_type is not None:
		A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     ).shape
	else:
		A__											=					hf_pointer.shape

	assert hf_shape == value.shape, (
	    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
	    F""" {value.shape} for {full_name}"""
	)

	if weight_type == "weight":
		A__											=					value
	elif weight_type == "weight_g":
		A__											=					value
	elif weight_type == "weight_v":
		A__											=					value
	elif weight_type == "bias":
		A__											=					value
	else:
		A__											=					value

	logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Union[str, Any]     )	-> str:






	"""simple docstring"""





	A__											=					[]
	A__											=					fairseq_model.state_dict()

	A__											=					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		A__											=					False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	hf_model.config.feat_extract_norm == "group",	)
			A__											=					True
		else:
			for key, mapped_key in MAPPING.items():
				A__											=					"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

				if key in name or (key.split("w2v_model."     )[-1] == name.split("."     )[0] and not is_finetuned):
					A__											=					True
					if "*" in mapped_key:
						A__											=					name.split(UpperCAmelCase_     )[0].split("."     )[-2]
						A__											=					mapped_key.replace("*",	UpperCAmelCase_     )
					if "weight_g" in name:
						A__											=					"weight_g"
					elif "weight_v" in name:
						A__											=					"weight_v"
					elif "weight" in name:
						A__											=					"weight"
					elif "bias" in name:
						A__											=					"bias"
					else:
						A__											=					None
					set_recursively(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_     )

	logger.warning(F"""Unused weights: {unused_weights}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Tuple     )	-> Tuple:






	"""simple docstring"""





	A__											=					full_name.split("conv_layers."     )[-1]
	A__											=					name.split("."     )
	A__											=					int(items[0]     )
	A__											=					int(items[1]     )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			A__											=					value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			A__											=					value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			A__											=					value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			A__											=					value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(UpperCAmelCase_     )

@torch.no_grad()
def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       Any=None,	UpperCAmelCase_ :       List[str]=None,	UpperCAmelCase_ :       Tuple=True     )	-> Optional[int]:






	"""simple docstring"""





	if config_path is not None:
		A__											=					HubertConfig.from_pretrained(UpperCAmelCase_     )
	else:
		A__											=					HubertConfig()

	if is_finetuned:
		if dict_path:
			A__											=					Dictionary.load(UpperCAmelCase_     )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__											=					target_dict.pad_index
			A__											=					target_dict.bos_index
			A__											=					target_dict.eos_index
			A__											=					len(target_dict.symbols     )
			A__											=					os.path.join(UpperCAmelCase_,	"vocab.json"     )
			if not os.path.isdir(UpperCAmelCase_     ):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_     )     )
				return
			os.makedirs(UpperCAmelCase_,	exist_ok=UpperCAmelCase_     )
			with open(UpperCAmelCase_,	"w",	encoding="utf-8"     ) as vocab_handle:
				json.dump(target_dict.indices,	UpperCAmelCase_     )
			A__											=					WavaVecaCTCTokenizer(
			    UpperCAmelCase_,	unk_token=target_dict.unk_word,	pad_token=target_dict.pad_word,	bos_token=target_dict.bos_word,	eos_token=target_dict.eos_word,	word_delimiter_token="|",	do_lower_case=UpperCAmelCase_,	)
			A__											=					True if config.feat_extract_norm == "layer" else False
			A__											=					WavaVecaFeatureExtractor(
			    feature_size=1,	sampling_rate=16000,	padding_value=0,	do_normalize=UpperCAmelCase_,	return_attention_mask=UpperCAmelCase_,	)
			A__											=					WavaVecaProcessor(feature_extractor=UpperCAmelCase_,	tokenizer=UpperCAmelCase_     )
			processor.save_pretrained(UpperCAmelCase_     )

		A__											=					HubertForCTC(UpperCAmelCase_     )
	else:
		A__											=					HubertModel(UpperCAmelCase_     )

	if is_finetuned:
		A__							,							A__							,							A__											=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path],	arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
	else:
		A__							,							A__							,							A__											=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

	A__											=					model[0].eval()

	recursively_load_weights(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )

	hf_wavavec.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
			parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
			parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
			parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
			parser.add_argument(
			    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
			)
			UpperCamelCase     =							parser.parse_args()
			convert_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
   __lowerCAmelCase           =				field(
       default="""NER"""  ,     metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
   __lowerCAmelCase           =				field(default=__lowerCAmelCase  ,     metadata={"""help""": """Set this flag to use fast tokenization."""}		)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,     )





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,     )
   __lowerCAmelCase           =				field(
       default=128  ,     metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }  ,     )
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)





def 			lowercase(	)		->	Optional[Any]:







 '''simple docstring'''
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCamelCase			  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
 if len(sys.argv       ) == 2 and sys.argv[1].endswith(""".json"""       ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
 else:
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir       )
     and os.listdir(training_args.output_dir       )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      """ --overwrite_output_dir to overcome."""       )

 UpperCamelCase			  =      import_module("""tasks"""       )
 try:
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					model_args.task_type       )
  UpperCamelCase			  =      token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,					datefmt="""%m/%d/%Y %H:%M:%S"""							,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,					)
 logger.warning(
     """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,					training_args.local_rank							,					training_args.device							,					training_args.n_gpu							,					bool(training_args.local_rank != -1       )							,					training_args.fpaa							,					)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank       ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s"""							,					UpperCamelCase_       )

 # Set seed
 set_seed(training_args.seed       )

 # Prepare CONLL-2003 task
 UpperCamelCase			  =      token_classification_task.get_labels(data_args.labels       )
 UpperCamelCase			  =      dict(enumerate(UpperCamelCase_       )       )
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCamelCase			  =      AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,					num_labels=UpperCamelCase_							,					idalabel=UpperCamelCase_							,					labelaid={label: i for i, label in enumerate(UpperCamelCase_       )}							,					cache_dir=model_args.cache_dir							,					)
 UpperCamelCase			  =      AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,					cache_dir=model_args.cache_dir							,					use_fast=model_args.use_fast							,					)
 UpperCamelCase			  =      AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path							,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path       )							,					config=UpperCamelCase_							,					cache_dir=model_args.cache_dir							,					)

 # Get datasets
 UpperCamelCase			  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.train							,					)
     if training_args.do_train
     else None
 )
 UpperCamelCase			  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.dev							,					)
     if training_args.do_eval
     else None
 )

 def align_predictions(UpperCamelCase_							,					UpperCamelCase_       ) -> Tuple[List[int], List[int]]:
  UpperCamelCase			  =      np.argmax(UpperCamelCase_							,					axis=2       )

  UpperCamelCase  ,     UpperCamelCase			  =      preds.shape

  UpperCamelCase			  =      [[] for _ in range(UpperCamelCase_       )]
  UpperCamelCase			  =      [[] for _ in range(UpperCamelCase_       )]

  for i in range(UpperCamelCase_       ):
   for j in range(UpperCamelCase_       ):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     out_label_list[i].append(label_map[label_ids[i][j]]       )
     preds_list[i].append(label_map[preds[i][j]]       )

  return preds_list, out_label_list

 def compute_metrics(UpperCamelCase_       ) -> Dict:
  UpperCamelCase  ,     UpperCamelCase			  =      align_predictions(p.predictions							,					p.label_ids       )
  return {
      "accuracy_score": accuracy_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "precision": precision_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "recall": recall_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "f1": fa_score(UpperCamelCase_							,					UpperCamelCase_       ),
  }

 # Data collator
 UpperCamelCase			  =      DataCollatorWithPadding(UpperCamelCase_							,					pad_to_multiple_of=8       ) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCamelCase			  =      Trainer(
     model=UpperCamelCase_							,					args=UpperCamelCase_							,					train_dataset=UpperCamelCase_							,					eval_dataset=UpperCamelCase_							,					compute_metrics=UpperCamelCase_							,					data_collator=UpperCamelCase_							,					)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_process_zero():
   tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
 UpperCamelCase			  =      {}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""       )

  UpperCamelCase			  =      trainer.evaluate()

  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""eval_results.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    logger.info("""***** Eval results *****"""       )
    for key, value in result.items():
     logger.info("""  %s = %s"""							,					UpperCamelCase_							,					UpperCamelCase_       )
     writer.write("""%s = %s\n""" % (key, value)       )

   results.update(UpperCamelCase_       )

    # Predict
 if training_args.do_predict:
  UpperCamelCase			  =      TokenClassificationDataset(
      token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.test							,					)

  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      trainer.predict(UpperCamelCase_       )
  UpperCamelCase  ,     UpperCamelCase			  =      align_predictions(UpperCamelCase_							,					UpperCamelCase_       )

  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""test_results.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    for key, value in metrics.items():
     logger.info("""  %s = %s"""							,					UpperCamelCase_							,					UpperCamelCase_       )
     writer.write("""%s = %s\n""" % (key, value)       )

        # Save predictions
  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""test_predictions.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    with open(os.path.join(data_args.data_dir							,					"""test.txt"""       )							,					"""r"""       ) as f:
     token_classification_task.write_predictions_to_file(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 return results





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()
